Overview

Dataset statistics

Number of variables46
Number of observations7728394
Missing cells12840498
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 GiB
Average record size in memory277.0 B

Variable types

Text8
Categorical10
DateTime3
Numeric12
Boolean13

Alerts

Country has constant value "US"Constant
Turning_Loop has constant value "False"Constant
Severity is highly imbalanced (55.4%)Imbalance
Amenity is highly imbalanced (90.3%)Imbalance
Bump is highly imbalanced (99.4%)Imbalance
Give_Way is highly imbalanced (95.7%)Imbalance
Junction is highly imbalanced (62.0%)Imbalance
No_Exit is highly imbalanced (97.5%)Imbalance
Railway is highly imbalanced (92.8%)Imbalance
Roundabout is highly imbalanced (99.9%)Imbalance
Station is highly imbalanced (82.5%)Imbalance
Stop is highly imbalanced (81.7%)Imbalance
Traffic_Calming is highly imbalanced (98.9%)Imbalance
End_Lat has 3402762 (44.0%) missing valuesMissing
End_Lng has 3402762 (44.0%) missing valuesMissing
Weather_Timestamp has 120228 (1.6%) missing valuesMissing
Temperature(F) has 163853 (2.1%) missing valuesMissing
Wind_Chill(F) has 1999019 (25.9%) missing valuesMissing
Humidity(%) has 174144 (2.3%) missing valuesMissing
Pressure(in) has 140679 (1.8%) missing valuesMissing
Visibility(mi) has 177098 (2.3%) missing valuesMissing
Wind_Direction has 175206 (2.3%) missing valuesMissing
Wind_Speed(mph) has 571233 (7.4%) missing valuesMissing
Precipitation(in) has 2203586 (28.5%) missing valuesMissing
Weather_Condition has 173459 (2.2%) missing valuesMissing
Distance(mi) is highly skewed (γ1 = 20.38575876)Skewed
Precipitation(in) is highly skewed (γ1 = 85.99914012)Skewed
ID has unique valuesUnique
Distance(mi) has 3302161 (42.7%) zerosZeros
Wind_Speed(mph) has 961643 (12.4%) zerosZeros
Precipitation(in) has 4991718 (64.6%) zerosZeros

Reproduction

Analysis started2024-06-18 19:05:25.798082
Analysis finished2024-06-18 19:21:10.380993
Duration15 minutes and 44.58 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct7728394
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:20.057325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.8574952
Min length3

Characters and Unicode

Total characters68454213
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7728394 ?
Unique (%)100.0%

Sample

1st rowA-1
2nd rowA-2
3rd rowA-3
4th rowA-4
5th rowA-5
ValueCountFrequency (%)
a-1 1
 
< 0.1%
a-19 1
 
< 0.1%
a-7 1
 
< 0.1%
a-8 1
 
< 0.1%
a-9 1
 
< 0.1%
a-10 1
 
< 0.1%
a-11 1
 
< 0.1%
a-12 1
 
< 0.1%
a-13 1
 
< 0.1%
a-14 1
 
< 0.1%
Other values (7728384) 7728384
> 99.9%
2024-06-18T14:21:25.512129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 7728394
11.3%
- 7728394
11.3%
1 5665623
8.3%
2 5665381
8.3%
4 5658416
8.3%
5 5650411
8.3%
3 5645102
8.2%
6 5639671
8.2%
7 5414701
7.9%
0 4553775
6.7%
Other values (2) 9104345
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68454213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 7728394
11.3%
- 7728394
11.3%
1 5665623
8.3%
2 5665381
8.3%
4 5658416
8.3%
5 5650411
8.3%
3 5645102
8.2%
6 5639671
8.2%
7 5414701
7.9%
0 4553775
6.7%
Other values (2) 9104345
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68454213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 7728394
11.3%
- 7728394
11.3%
1 5665623
8.3%
2 5665381
8.3%
4 5658416
8.3%
5 5650411
8.3%
3 5645102
8.2%
6 5639671
8.2%
7 5414701
7.9%
0 4553775
6.7%
Other values (2) 9104345
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68454213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 7728394
11.3%
- 7728394
11.3%
1 5665623
8.3%
2 5665381
8.3%
4 5658416
8.3%
5 5650411
8.3%
3 5645102
8.2%
6 5639671
8.2%
7 5414701
7.9%
0 4553775
6.7%
Other values (2) 9104345
13.3%

Source
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
Source1
4325632 
Source2
3305373 
Source3
 
97389

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters54098758
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSource2
2nd rowSource2
3rd rowSource2
4th rowSource2
5th rowSource2

Common Values

ValueCountFrequency (%)
Source1 4325632
56.0%
Source2 3305373
42.8%
Source3 97389
 
1.3%

Length

2024-06-18T14:21:25.697893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:25.823327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
source1 4325632
56.0%
source2 3305373
42.8%
source3 97389
 
1.3%

Most occurring characters

ValueCountFrequency (%)
S 7728394
14.3%
o 7728394
14.3%
u 7728394
14.3%
r 7728394
14.3%
c 7728394
14.3%
e 7728394
14.3%
1 4325632
8.0%
2 3305373
6.1%
3 97389
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54098758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 7728394
14.3%
o 7728394
14.3%
u 7728394
14.3%
r 7728394
14.3%
c 7728394
14.3%
e 7728394
14.3%
1 4325632
8.0%
2 3305373
6.1%
3 97389
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54098758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 7728394
14.3%
o 7728394
14.3%
u 7728394
14.3%
r 7728394
14.3%
c 7728394
14.3%
e 7728394
14.3%
1 4325632
8.0%
2 3305373
6.1%
3 97389
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54098758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 7728394
14.3%
o 7728394
14.3%
u 7728394
14.3%
r 7728394
14.3%
c 7728394
14.3%
e 7728394
14.3%
1 4325632
8.0%
2 3305373
6.1%
3 97389
 
0.2%

Severity
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2
6156981 
3
1299337 
4
 
204710
1
 
67366

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7728394
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Length

2024-06-18T14:21:25.953889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:26.063751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%
Distinct5801064
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
Minimum2016-01-14 20:18:33
Maximum2023-03-31 23:30:00
2024-06-18T14:21:26.200682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:21:26.352451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct6463024
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
Minimum2016-02-08 06:37:08
Maximum2023-03-31 23:59:00
2024-06-18T14:21:26.503416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:21:26.662040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Start_Lat
Real number (ℝ)

Distinct2428358
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.201195
Minimum24.5548
Maximum49.002201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:26.858708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.5548
5-th percentile27.111647
Q133.399631
median35.823974
Q340.084959
95-th percentile44.858648
Maximum49.002201
Range24.447401
Interquartile range (IQR)6.6853275

Descriptive statistics

Standard deviation5.0760791
Coefficient of variation (CV)0.14021855
Kurtosis-0.53205376
Mean36.201195
Median Absolute Deviation (MAD)3.3899255
Skewness-0.072220725
Sum2.7977709 × 108
Variance25.766579
MonotonicityNot monotonic
2024-06-18T14:21:27.013970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.808498 570
 
< 0.1%
33.941364 568
 
< 0.1%
34.858849 545
 
< 0.1%
42.476501 534
 
< 0.1%
33.744976 533
 
< 0.1%
34.858925 495
 
< 0.1%
40.847923 473
 
< 0.1%
34.039394 462
 
< 0.1%
33.876289 458
 
< 0.1%
25.789072 441
 
< 0.1%
Other values (2428348) 7723315
99.9%
ValueCountFrequency (%)
24.5548 1
< 0.1%
24.555269 1
< 0.1%
24.5574 1
< 0.1%
24.559731 1
< 0.1%
24.55987 1
< 0.1%
24.560246 1
< 0.1%
24.560688 1
< 0.1%
24.562117 1
< 0.1%
24.563089 1
< 0.1%
24.566027 1
< 0.1%
ValueCountFrequency (%)
49.002201 1
< 0.1%
49.000759 1
< 0.1%
49.00058 1
< 0.1%
49.00056 1
< 0.1%
49.000504 2
< 0.1%
49.00049329 1
< 0.1%
49.000269 1
< 0.1%
49.00026 1
< 0.1%
48.999901 1
< 0.1%
48.999569 1
< 0.1%

Start_Lng
Real number (ℝ)

Distinct2482533
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-94.702545
Minimum-124.62383
Maximum-67.113167
Zeros0
Zeros (%)0.0%
Negative7728394
Negative (%)100.0%
Memory size59.0 MiB
2024-06-18T14:21:27.204356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-124.62383
5-th percentile-122.19164
Q1-117.2194
median-87.766616
Q3-80.353676
95-th percentile-73.956087
Maximum-67.113167
Range57.510666
Interquartile range (IQR)36.86572

Descriptive statistics

Standard deviation17.391756
Coefficient of variation (CV)-0.18364613
Kurtosis-1.3631024
Mean-94.702545
Median Absolute Deviation (MAD)9.968301
Skewness-0.48291963
Sum-7.3189858 × 108
Variance302.47319
MonotonicityNot monotonic
2024-06-18T14:21:27.350304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.366852 578
 
< 0.1%
-118.096634 562
 
< 0.1%
-82.260422 545
 
< 0.1%
-84.390343 534
 
< 0.1%
-83.111794 534
 
< 0.1%
-73.942825 514
 
< 0.1%
-80.165855 513
 
< 0.1%
-82.259857 497
 
< 0.1%
-118.368263 495
 
< 0.1%
-80.210114 476
 
< 0.1%
Other values (2482523) 7723146
99.9%
ValueCountFrequency (%)
-124.623833 1
 
< 0.1%
-124.548074 2
< 0.1%
-124.541015 1
 
< 0.1%
-124.539056 1
 
< 0.1%
-124.535893 1
 
< 0.1%
-124.535726 1
 
< 0.1%
-124.534439 1
 
< 0.1%
-124.531602 4
< 0.1%
-124.512297 1
 
< 0.1%
-124.511949 1
 
< 0.1%
ValueCountFrequency (%)
-67.113167 1
< 0.1%
-67.403551 1
< 0.1%
-67.48413 1
< 0.1%
-67.553307 1
< 0.1%
-67.606864 1
< 0.1%
-67.606875 1
< 0.1%
-67.614387 1
< 0.1%
-67.626576 1
< 0.1%
-67.70337 1
< 0.1%
-67.709053 1
< 0.1%

End_Lat
Real number (ℝ)

MISSING 

Distinct1568172
Distinct (%)36.3%
Missing3402762
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean36.261829
Minimum24.566013
Maximum49.075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:27.528488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.566013
5-th percentile26.029643
Q133.46207
median36.183495
Q340.17892
95-th percentile44.981551
Maximum49.075
Range24.508987
Interquartile range (IQR)6.7168502

Descriptive statistics

Standard deviation5.2729045
Coefficient of variation (CV)0.14541199
Kurtosis-0.55709151
Mean36.261829
Median Absolute Deviation (MAD)3.4407145
Skewness-0.15821723
Sum1.5685533 × 108
Variance27.803522
MonotonicityNot monotonic
2024-06-18T14:21:27.687739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.450015 1039
 
< 0.1%
25.701774 860
 
< 0.1%
25.684322 836
 
< 0.1%
28.449928 794
 
< 0.1%
25.686252 720
 
< 0.1%
25.924771 712
 
< 0.1%
25.889378 712
 
< 0.1%
25.73316 680
 
< 0.1%
28.45019 656
 
< 0.1%
28.42136 654
 
< 0.1%
Other values (1568162) 4317969
55.9%
(Missing) 3402762
44.0%
ValueCountFrequency (%)
24.566013 1
 
< 0.1%
24.569978 3
< 0.1%
24.570107 4
< 0.1%
24.57011 1
 
< 0.1%
24.57018 1
 
< 0.1%
24.57029 1
 
< 0.1%
24.57036 1
 
< 0.1%
24.570461 1
 
< 0.1%
24.57124 1
 
< 0.1%
24.57126 1
 
< 0.1%
ValueCountFrequency (%)
49.075 1
 
< 0.1%
49.00222329 1
 
< 0.1%
49.00214 1
 
< 0.1%
49.002025 2
< 0.1%
49.000769 2
< 0.1%
49.00076 3
< 0.1%
49.000641 1
 
< 0.1%
49.00056 1
 
< 0.1%
49.000025 1
 
< 0.1%
48.999966 2
< 0.1%

End_Lng
Real number (ℝ)

MISSING 

Distinct1605789
Distinct (%)37.1%
Missing3402762
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean-95.72557
Minimum-124.54575
Maximum-67.109242
Zeros0
Zeros (%)0.0%
Negative4325632
Negative (%)56.0%
Memory size59.0 MiB
2024-06-18T14:21:27.860191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-124.54575
5-th percentile-122.26437
Q1-117.75434
median-88.02789
Q3-80.247086
95-th percentile-74.023676
Maximum-67.109242
Range57.436506
Interquartile range (IQR)37.507258

Descriptive statistics

Standard deviation18.107928
Coefficient of variation (CV)-0.189165
Kurtosis-1.5556514
Mean-95.72557
Median Absolute Deviation (MAD)10.95307
Skewness-0.36549222
Sum-4.1407359 × 108
Variance327.89704
MonotonicityNot monotonic
2024-06-18T14:21:28.012254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-81.471375 1037
 
< 0.1%
-80.334179 860
 
< 0.1%
-80.416621 836
 
< 0.1%
-81.477219 794
 
< 0.1%
-80.416521 721
 
< 0.1%
-80.293318 712
 
< 0.1%
-80.336612 679
 
< 0.1%
-81.399777 658
 
< 0.1%
-78.680237 645
 
< 0.1%
-81.47766 637
 
< 0.1%
Other values (1605779) 4318053
55.9%
(Missing) 3402762
44.0%
ValueCountFrequency (%)
-124.545748 2
< 0.1%
-124.544508 1
 
< 0.1%
-124.543727 1
 
< 0.1%
-124.539056 1
 
< 0.1%
-124.535893 1
 
< 0.1%
-124.535726 3
< 0.1%
-124.531602 1
 
< 0.1%
-124.512297 1
 
< 0.1%
-124.509263 1
 
< 0.1%
-124.497829 1
 
< 0.1%
ValueCountFrequency (%)
-67.109242 1
< 0.1%
-67.40355 1
< 0.1%
-67.48413 1
< 0.1%
-67.606864 1
< 0.1%
-67.62034 1
< 0.1%
-67.626576 1
< 0.1%
-67.626605 1
< 0.1%
-67.706448 1
< 0.1%
-67.739817 1
< 0.1%
-67.78734 1
< 0.1%

Distance(mi)
Real number (ℝ)

SKEWED  ZEROS 

Distinct22382
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56184228
Minimum0
Maximum441.75
Zeros3302161
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:28.169181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.464
95-th percentile2.67
Maximum441.75
Range441.75
Interquartile range (IQR)0.464

Descriptive statistics

Standard deviation1.7768106
Coefficient of variation (CV)3.1624722
Kurtosis1649.5954
Mean0.56184228
Median Absolute Deviation (MAD)0.03
Skewness20.385759
Sum4342138.5
Variance3.1570559
MonotonicityNot monotonic
2024-06-18T14:21:28.313234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3302161
42.7%
0.01 262493
 
3.4%
0.008 14558
 
0.2%
0.009 13836
 
0.2%
0.009999999776 13367
 
0.2%
0.007 12413
 
0.2%
0.011 11625
 
0.2%
0.03 11322
 
0.1%
0.024 11002
 
0.1%
0.028 10927
 
0.1%
Other values (22372) 4064690
52.6%
ValueCountFrequency (%)
0 3302161
42.7%
0.001 5585
 
0.1%
0.002 3078
 
< 0.1%
0.003 4263
 
0.1%
0.004 6337
 
0.1%
0.005 8253
 
0.1%
0.006 10121
 
0.1%
0.007 12413
 
0.2%
0.008 14558
 
0.2%
0.009 13836
 
0.2%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
333.6300049 1
< 0.1%
254.3999939 1
< 0.1%
251.2200012 1
< 0.1%
242.3399963 1
< 0.1%
227.2100067 1
< 0.1%
224.5899963 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%
Distinct3761578
Distinct (%)48.7%
Missing5
Missing (%)< 0.1%
Memory size59.0 MiB
2024-06-18T14:21:31.475613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length679
Median length456
Mean length68.837155
Min length2

Characters and Unicode

Total characters532000315
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2704451 ?
Unique (%)35.0%

Sample

1st rowRight lane blocked due to accident on I-70 Eastbound at Exit 41 OH-235 State Route 4.
2nd rowAccident on Brice Rd at Tussing Rd. Expect delays.
3rd rowAccident on OH-32 State Route 32 Westbound at Dela Palma Rd. Expect delays.
4th rowAccident on I-75 Southbound at Exits 52 52B US-35. Expect delays.
5th rowAccident on McEwen Rd at OH-725 Miamisburg Centerville Rd. Expect delays.
ValueCountFrequency (%)
on 6287667
 
6.6%
accident 5703508
 
6.0%
to 4570428
 
4.8%
at 3662924
 
3.9%
due 2871138
 
3.0%
rd 2602371
 
2.7%
2186578
 
2.3%
near 1721796
 
1.8%
blocked 1720312
 
1.8%
from 1607181
 
1.7%
Other values (225147) 61729359
65.2%
2024-06-18T14:21:32.363489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86934796
16.3%
t 34426190
 
6.5%
e 31991488
 
6.0%
n 30314954
 
5.7%
o 28376352
 
5.3%
a 23052293
 
4.3%
d 22676534
 
4.3%
i 21063299
 
4.0%
c 19870368
 
3.7%
r 16094461
 
3.0%
Other values (95) 217199580
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532000315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
86934796
16.3%
t 34426190
 
6.5%
e 31991488
 
6.0%
n 30314954
 
5.7%
o 28376352
 
5.3%
a 23052293
 
4.3%
d 22676534
 
4.3%
i 21063299
 
4.0%
c 19870368
 
3.7%
r 16094461
 
3.0%
Other values (95) 217199580
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532000315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
86934796
16.3%
t 34426190
 
6.5%
e 31991488
 
6.0%
n 30314954
 
5.7%
o 28376352
 
5.3%
a 23052293
 
4.3%
d 22676534
 
4.3%
i 21063299
 
4.0%
c 19870368
 
3.7%
r 16094461
 
3.0%
Other values (95) 217199580
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532000315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
86934796
16.3%
t 34426190
 
6.5%
e 31991488
 
6.0%
n 30314954
 
5.7%
o 28376352
 
5.3%
a 23052293
 
4.3%
d 22676534
 
4.3%
i 21063299
 
4.0%
c 19870368
 
3.7%
r 16094461
 
3.0%
Other values (95) 217199580
40.8%

Street
Text

Distinct336306
Distinct (%)4.4%
Missing10869
Missing (%)0.1%
Memory size59.0 MiB
2024-06-18T14:21:32.842500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length47
Mean length11.062818
Min length1

Characters and Unicode

Total characters85377573
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129934 ?
Unique (%)1.7%

Sample

1st rowI-70 E
2nd rowBrice Rd
3rd rowState Route 32
4th rowI-75 S
5th rowMiamisburg Centerville Rd
ValueCountFrequency (%)
n 1199552
 
6.3%
s 1194304
 
6.2%
rd 1159617
 
6.0%
w 941905
 
4.9%
e 931753
 
4.9%
st 684198
 
3.6%
ave 640512
 
3.3%
blvd 343950
 
1.8%
fwy 330007
 
1.7%
dr 327207
 
1.7%
Other values (74656) 11416775
59.6%
2024-06-18T14:21:33.376714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

City
Text

Distinct13678
Distinct (%)0.2%
Missing253
Missing (%)< 0.1%
Memory size59.0 MiB
2024-06-18T14:21:33.716367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length8.7755685
Min length3

Characters and Unicode

Total characters67818831
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1023 ?
Unique (%)< 0.1%

Sample

1st rowDayton
2nd rowReynoldsburg
3rd rowWilliamsburg
4th rowDayton
5th rowDayton
ValueCountFrequency (%)
san 222025
 
2.2%
miami 207501
 
2.1%
city 196884
 
2.0%
houston 169689
 
1.7%
los 169053
 
1.7%
angeles 156702
 
1.6%
charlotte 139395
 
1.4%
dallas 131585
 
1.3%
orlando 109733
 
1.1%
beach 98038
 
1.0%
Other values (10808) 8345041
83.9%
2024-06-18T14:21:34.221758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6545547
 
9.7%
e 5932168
 
8.7%
o 5228124
 
7.7%
n 5137103
 
7.6%
l 4621065
 
6.8%
i 4263043
 
6.3%
r 3924811
 
5.8%
t 3811793
 
5.6%
s 3182527
 
4.7%
2217505
 
3.3%
Other values (56) 22955145
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67818831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6545547
 
9.7%
e 5932168
 
8.7%
o 5228124
 
7.7%
n 5137103
 
7.6%
l 4621065
 
6.8%
i 4263043
 
6.3%
r 3924811
 
5.8%
t 3811793
 
5.6%
s 3182527
 
4.7%
2217505
 
3.3%
Other values (56) 22955145
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67818831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6545547
 
9.7%
e 5932168
 
8.7%
o 5228124
 
7.7%
n 5137103
 
7.6%
l 4621065
 
6.8%
i 4263043
 
6.3%
r 3924811
 
5.8%
t 3811793
 
5.6%
s 3182527
 
4.7%
2217505
 
3.3%
Other values (56) 22955145
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67818831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6545547
 
9.7%
e 5932168
 
8.7%
o 5228124
 
7.7%
n 5137103
 
7.6%
l 4621065
 
6.8%
i 4263043
 
6.3%
r 3924811
 
5.8%
t 3811793
 
5.6%
s 3182527
 
4.7%
2217505
 
3.3%
Other values (56) 22955145
33.8%

County
Text

Distinct1871
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:34.579059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length30
Median length23
Mean length8.0643539
Min length3

Characters and Unicode

Total characters62324504
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowMontgomery
2nd rowFranklin
3rd rowClermont
4th rowMontgomery
5th rowMontgomery
ValueCountFrequency (%)
los 526853
 
5.6%
angeles 526851
 
5.6%
san 311726
 
3.3%
miami-dade 251601
 
2.7%
orange 241275
 
2.6%
harris 181196
 
1.9%
dallas 157024
 
1.7%
mecklenburg 147265
 
1.6%
montgomery 136788
 
1.5%
wake 117890
 
1.3%
Other values (1772) 6803587
72.4%
2024-06-18T14:21:35.065548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6748757
 
10.8%
e 6490587
 
10.4%
n 4953741
 
7.9%
o 4391707
 
7.0%
r 4005541
 
6.4%
s 3454789
 
5.5%
i 3316153
 
5.3%
l 3243086
 
5.2%
t 2162391
 
3.5%
g 1805031
 
2.9%
Other values (49) 21752721
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62324504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6748757
 
10.8%
e 6490587
 
10.4%
n 4953741
 
7.9%
o 4391707
 
7.0%
r 4005541
 
6.4%
s 3454789
 
5.5%
i 3316153
 
5.3%
l 3243086
 
5.2%
t 2162391
 
3.5%
g 1805031
 
2.9%
Other values (49) 21752721
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62324504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6748757
 
10.8%
e 6490587
 
10.4%
n 4953741
 
7.9%
o 4391707
 
7.0%
r 4005541
 
6.4%
s 3454789
 
5.5%
i 3316153
 
5.3%
l 3243086
 
5.2%
t 2162391
 
3.5%
g 1805031
 
2.9%
Other values (49) 21752721
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62324504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6748757
 
10.8%
e 6490587
 
10.4%
n 4953741
 
7.9%
o 4391707
 
7.0%
r 4005541
 
6.4%
s 3454789
 
5.5%
i 3316153
 
5.3%
l 3243086
 
5.2%
t 2162391
 
3.5%
g 1805031
 
2.9%
Other values (49) 21752721
34.9%

State
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
CA
1741433 
FL
880192 
TX
582837 
SC
382557 
NY
 
347960
Other values (44)
3793415 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters15456788
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowOH
3rd rowOH
4th rowOH
5th rowOH

Common Values

ValueCountFrequency (%)
CA 1741433
22.5%
FL 880192
 
11.4%
TX 582837
 
7.5%
SC 382557
 
5.0%
NY 347960
 
4.5%
NC 338199
 
4.4%
VA 303301
 
3.9%
PA 296620
 
3.8%
MN 192084
 
2.5%
OR 179660
 
2.3%
Other values (39) 2483551
32.1%

Length

2024-06-18T14:21:35.224570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 1741433
22.5%
fl 880192
 
11.4%
tx 582837
 
7.5%
sc 382557
 
5.0%
ny 347960
 
4.5%
nc 338199
 
4.4%
va 303301
 
3.9%
pa 296620
 
3.8%
mn 192084
 
2.5%
or 179660
 
2.3%
Other values (39) 2483551
32.1%

Most occurring characters

ValueCountFrequency (%)
A 3151246
20.4%
C 2642709
17.1%
N 1328134
8.6%
L 1299895
8.4%
T 947731
 
6.1%
F 880192
 
5.7%
M 690711
 
4.5%
X 582837
 
3.8%
O 549630
 
3.6%
I 487715
 
3.2%
Other values (14) 2895988
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3151246
20.4%
C 2642709
17.1%
N 1328134
8.6%
L 1299895
8.4%
T 947731
 
6.1%
F 880192
 
5.7%
M 690711
 
4.5%
X 582837
 
3.8%
O 549630
 
3.6%
I 487715
 
3.2%
Other values (14) 2895988
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3151246
20.4%
C 2642709
17.1%
N 1328134
8.6%
L 1299895
8.4%
T 947731
 
6.1%
F 880192
 
5.7%
M 690711
 
4.5%
X 582837
 
3.8%
O 549630
 
3.6%
I 487715
 
3.2%
Other values (14) 2895988
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3151246
20.4%
C 2642709
17.1%
N 1328134
8.6%
L 1299895
8.4%
T 947731
 
6.1%
F 880192
 
5.7%
M 690711
 
4.5%
X 582837
 
3.8%
O 549630
 
3.6%
I 487715
 
3.2%
Other values (14) 2895988
18.7%
Distinct825094
Distinct (%)10.7%
Missing1915
Missing (%)< 0.1%
Memory size59.0 MiB
2024-06-18T14:21:36.177474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length6.4678536
Min length5

Characters and Unicode

Total characters49973735
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique450606 ?
Unique (%)5.8%

Sample

1st row45424
2nd row43068-3402
3rd row45176
4th row45417
5th row45459
ValueCountFrequency (%)
91761 11247
 
0.1%
91706 10022
 
0.1%
92407 8922
 
0.1%
92507 8850
 
0.1%
33186 8375
 
0.1%
32819 7461
 
0.1%
91765 7377
 
0.1%
33169 7106
 
0.1%
90023 7066
 
0.1%
92324 7010
 
0.1%
Other values (825084) 7643043
98.9%
2024-06-18T14:21:37.102912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
US
7728394 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters15456788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 7728394
100.0%

Length

2024-06-18T14:21:37.277265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:37.372253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
us 7728394
100.0%

Most occurring characters

ValueCountFrequency (%)
U 7728394
50.0%
S 7728394
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 7728394
50.0%
S 7728394
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 7728394
50.0%
S 7728394
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15456788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 7728394
50.0%
S 7728394
50.0%

Timezone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing7808
Missing (%)0.1%
Memory size59.0 MiB
US/Eastern
3580167 
US/Pacific
2062984 
US/Central
1645616 
US/Mountain
431819 

Length

Max length11
Median length10
Mean length10.055931
Min length10

Characters and Unicode

Total characters77637679
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS/Eastern
2nd rowUS/Eastern
3rd rowUS/Eastern
4th rowUS/Eastern
5th rowUS/Eastern

Common Values

ValueCountFrequency (%)
US/Eastern 3580167
46.3%
US/Pacific 2062984
26.7%
US/Central 1645616
21.3%
US/Mountain 431819
 
5.6%
(Missing) 7808
 
0.1%

Length

2024-06-18T14:21:37.479867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:37.594782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
us/eastern 3580167
46.4%
us/pacific 2062984
26.7%
us/central 1645616
21.3%
us/mountain 431819
 
5.6%

Most occurring characters

ValueCountFrequency (%)
U 7720586
9.9%
a 7720586
9.9%
/ 7720586
9.9%
S 7720586
9.9%
n 6089421
 
7.8%
t 5657602
 
7.3%
e 5225783
 
6.7%
r 5225783
 
6.7%
i 4557787
 
5.9%
c 4125968
 
5.3%
Other values (9) 15872991
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77637679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 7720586
9.9%
a 7720586
9.9%
/ 7720586
9.9%
S 7720586
9.9%
n 6089421
 
7.8%
t 5657602
 
7.3%
e 5225783
 
6.7%
r 5225783
 
6.7%
i 4557787
 
5.9%
c 4125968
 
5.3%
Other values (9) 15872991
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77637679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 7720586
9.9%
a 7720586
9.9%
/ 7720586
9.9%
S 7720586
9.9%
n 6089421
 
7.8%
t 5657602
 
7.3%
e 5225783
 
6.7%
r 5225783
 
6.7%
i 4557787
 
5.9%
c 4125968
 
5.3%
Other values (9) 15872991
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77637679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 7720586
9.9%
a 7720586
9.9%
/ 7720586
9.9%
S 7720586
9.9%
n 6089421
 
7.8%
t 5657602
 
7.3%
e 5225783
 
6.7%
r 5225783
 
6.7%
i 4557787
 
5.9%
c 4125968
 
5.3%
Other values (9) 15872991
20.4%
Distinct2045
Distinct (%)< 0.1%
Missing22635
Missing (%)0.3%
Memory size59.0 MiB
2024-06-18T14:21:38.029092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters30823036
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowKFFO
2nd rowKCMH
3rd rowKI69
4th rowKDAY
5th rowKMGY
ValueCountFrequency (%)
kcqt 118332
 
1.5%
krdu 107267
 
1.4%
kmcj 101786
 
1.3%
kbna 98926
 
1.3%
kclt 97273
 
1.3%
korl 82480
 
1.1%
kmia 81358
 
1.1%
kbtr 78304
 
1.0%
kopf 70665
 
0.9%
kdal 69353
 
0.9%
Other values (2035) 6800015
88.2%
2024-06-18T14:21:38.535023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 8170466
26.5%
A 1664545
 
5.4%
C 1572716
 
5.1%
M 1370216
 
4.4%
T 1362405
 
4.4%
L 1307926
 
4.2%
S 1296708
 
4.2%
D 1235661
 
4.0%
R 1192091
 
3.9%
O 1119180
 
3.6%
Other values (26) 10531122
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30823036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 8170466
26.5%
A 1664545
 
5.4%
C 1572716
 
5.1%
M 1370216
 
4.4%
T 1362405
 
4.4%
L 1307926
 
4.2%
S 1296708
 
4.2%
D 1235661
 
4.0%
R 1192091
 
3.9%
O 1119180
 
3.6%
Other values (26) 10531122
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30823036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 8170466
26.5%
A 1664545
 
5.4%
C 1572716
 
5.1%
M 1370216
 
4.4%
T 1362405
 
4.4%
L 1307926
 
4.2%
S 1296708
 
4.2%
D 1235661
 
4.0%
R 1192091
 
3.9%
O 1119180
 
3.6%
Other values (26) 10531122
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30823036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 8170466
26.5%
A 1664545
 
5.4%
C 1572716
 
5.1%
M 1370216
 
4.4%
T 1362405
 
4.4%
L 1307926
 
4.2%
S 1296708
 
4.2%
D 1235661
 
4.0%
R 1192091
 
3.9%
O 1119180
 
3.6%
Other values (26) 10531122
34.2%

Weather_Timestamp
Date

MISSING 

Distinct941331
Distinct (%)12.4%
Missing120228
Missing (%)1.6%
Memory size59.0 MiB
Minimum2016-01-14 19:51:00
Maximum2023-03-31 23:53:00
2024-06-18T14:21:38.713821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:21:38.864583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Temperature(F)
Real number (ℝ)

MISSING 

Distinct860
Distinct (%)< 0.1%
Missing163853
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean61.663286
Minimum-89
Maximum207
Zeros2775
Zeros (%)< 0.1%
Negative19478
Negative (%)0.3%
Memory size59.0 MiB
2024-06-18T14:21:39.006081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile28
Q149
median64
Q376
95-th percentile89
Maximum207
Range296
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.013653
Coefficient of variation (CV)0.30834642
Kurtosis-0.0012691043
Mean61.663286
Median Absolute Deviation (MAD)13
Skewness-0.513733
Sum4.6645445 × 108
Variance361.51901
MonotonicityNot monotonic
2024-06-18T14:21:39.157386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 170991
 
2.2%
73 170898
 
2.2%
68 163767
 
2.1%
72 160498
 
2.1%
75 158448
 
2.1%
70 155568
 
2.0%
63 149787
 
1.9%
59 149017
 
1.9%
64 148466
 
1.9%
79 147140
 
1.9%
Other values (850) 5989961
77.5%
(Missing) 163853
 
2.1%
ValueCountFrequency (%)
-89 10
< 0.1%
-77.8 11
< 0.1%
-58 1
 
< 0.1%
-50 1
 
< 0.1%
-45 1
 
< 0.1%
-44 1
 
< 0.1%
-40 2
 
< 0.1%
-38 3
 
< 0.1%
-37 5
< 0.1%
-36 4
 
< 0.1%
ValueCountFrequency (%)
207 3
< 0.1%
203 1
 
< 0.1%
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
162 2
 
< 0.1%

Wind_Chill(F)
Real number (ℝ)

MISSING 

Distinct1001
Distinct (%)< 0.1%
Missing1999019
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean58.251048
Minimum-89
Maximum207
Zeros3948
Zeros (%)0.1%
Negative64428
Negative (%)0.8%
Memory size59.0 MiB
2024-06-18T14:21:39.298592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile18
Q143
median62
Q375
95-th percentile88
Maximum207
Range296
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.389832
Coefficient of variation (CV)0.38436788
Kurtosis0.15453367
Mean58.251048
Median Absolute Deviation (MAD)15
Skewness-0.67278577
Sum3.337421 × 108
Variance501.30457
MonotonicityNot monotonic
2024-06-18T14:21:39.442801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 133584
 
1.7%
72 125378
 
1.6%
75 123065
 
1.6%
77 122062
 
1.6%
70 120727
 
1.6%
63 115954
 
1.5%
79 115703
 
1.5%
68 115211
 
1.5%
64 114934
 
1.5%
66 112160
 
1.5%
Other values (991) 4530597
58.6%
(Missing) 1999019
25.9%
ValueCountFrequency (%)
-89 10
< 0.1%
-80 1
 
< 0.1%
-69 1
 
< 0.1%
-65.9 1
 
< 0.1%
-63 7
< 0.1%
-59 4
 
< 0.1%
-58 2
 
< 0.1%
-55.1 1
 
< 0.1%
-55 2
 
< 0.1%
-54.1 1
 
< 0.1%
ValueCountFrequency (%)
207 3
 
< 0.1%
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
162 2
 
< 0.1%
140 12
< 0.1%
138 1
 
< 0.1%
136 3
 
< 0.1%
128 2
 
< 0.1%

Humidity(%)
Real number (ℝ)

MISSING 

Distinct100
Distinct (%)< 0.1%
Missing174144
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean64.831041
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:39.590132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q148
median67
Q384
95-th percentile97
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.820968
Coefficient of variation (CV)0.3520068
Kurtosis-0.7234553
Mean64.831041
Median Absolute Deviation (MAD)18
Skewness-0.39484242
Sum4.897499 × 108
Variance520.79656
MonotonicityNot monotonic
2024-06-18T14:21:39.744900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 290345
 
3.8%
100 286680
 
3.7%
87 169582
 
2.2%
90 166492
 
2.2%
89 140593
 
1.8%
96 134809
 
1.7%
84 126652
 
1.6%
81 126612
 
1.6%
82 124386
 
1.6%
86 121255
 
1.6%
Other values (90) 5866844
75.9%
(Missing) 174144
 
2.3%
ValueCountFrequency (%)
1 49
 
< 0.1%
2 189
 
< 0.1%
3 670
 
< 0.1%
4 2167
 
< 0.1%
5 4113
 
0.1%
6 6010
0.1%
7 8072
0.1%
8 9661
0.1%
9 11116
0.1%
10 13495
0.2%
ValueCountFrequency (%)
100 286680
3.7%
99 14262
 
0.2%
98 6977
 
0.1%
97 88156
 
1.1%
96 134809
1.7%
95 9612
 
0.1%
94 119009
1.5%
93 290345
3.8%
92 66899
 
0.9%
91 37561
 
0.5%

Pressure(in)
Real number (ℝ)

MISSING 

Distinct1144
Distinct (%)< 0.1%
Missing140679
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean29.538986
Minimum0
Maximum58.63
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:39.885937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.98
Q129.37
median29.86
Q330.03
95-th percentile30.26
Maximum58.63
Range58.63
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation1.0061898
Coefficient of variation (CV)0.034063113
Kurtosis21.841661
Mean29.538986
Median Absolute Deviation (MAD)0.24
Skewness-3.6387719
Sum2.241334 × 108
Variance1.0124179
MonotonicityNot monotonic
2024-06-18T14:21:40.034198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 123289
 
1.6%
29.99 121836
 
1.6%
30.01 119735
 
1.5%
29.94 119000
 
1.5%
30.04 113905
 
1.5%
29.97 112320
 
1.5%
30.03 110898
 
1.4%
29.91 110700
 
1.4%
30 109999
 
1.4%
29.95 109924
 
1.4%
Other values (1134) 6436109
83.3%
(Missing) 140679
 
1.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.02 1
 
< 0.1%
0.12 1
 
< 0.1%
0.29 2
 
< 0.1%
0.3 6
< 0.1%
0.39 1
 
< 0.1%
2.98 1
 
< 0.1%
2.99 9
< 0.1%
3 2
 
< 0.1%
3.01 2
 
< 0.1%
ValueCountFrequency (%)
58.63 9
< 0.1%
58.39 2
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 4
< 0.1%
58.04 3
 
< 0.1%
58.03 1
 
< 0.1%
57.74 1
 
< 0.1%
57.54 2
 
< 0.1%
56.54 2
 
< 0.1%

Visibility(mi)
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)< 0.1%
Missing177098
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean9.0903764
Minimum0
Maximum140
Zeros7679
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:40.193856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6883159
Coefficient of variation (CV)0.29573208
Kurtosis81.893919
Mean9.0903764
Median Absolute Deviation (MAD)0
Skewness2.3166628
Sum68644123
Variance7.2270425
MonotonicityNot monotonic
2024-06-18T14:21:40.340243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 6070231
78.5%
7 217027
 
2.8%
9 188529
 
2.4%
8 149975
 
1.9%
5 144153
 
1.9%
6 126586
 
1.6%
2 121785
 
1.6%
4 119770
 
1.5%
3 117493
 
1.5%
1 102557
 
1.3%
Other values (82) 193190
 
2.5%
(Missing) 177098
 
2.3%
ValueCountFrequency (%)
0 7679
 
0.1%
0.06 323
 
< 0.1%
0.1 1287
 
< 0.1%
0.12 1775
 
< 0.1%
0.19 41
 
< 0.1%
0.2 12105
0.2%
0.25 27344
0.4%
0.31 4
 
< 0.1%
0.38 337
 
< 0.1%
0.4 98
 
< 0.1%
ValueCountFrequency (%)
140 3
 
< 0.1%
130 1
 
< 0.1%
120 5
 
< 0.1%
111 3
 
< 0.1%
110 1
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 47
< 0.1%
98 1
 
< 0.1%
90 13
 
< 0.1%

Wind_Direction
Categorical

MISSING 

Distinct24
Distinct (%)< 0.1%
Missing175206
Missing (%)2.3%
Memory size59.0 MiB
CALM
961624 
S
 
419989
SSW
 
384840
W
 
383913
WNW
 
378781
Other values (19)
5024041 

Length

Max length8
Median length5
Mean length2.8361859
Min length1

Characters and Unicode

Total characters21422245
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalm
2nd rowCalm
3rd rowSW
4th rowSW
5th rowSW

Common Values

ValueCountFrequency (%)
CALM 961624
 
12.4%
S 419989
 
5.4%
SSW 384840
 
5.0%
W 383913
 
5.0%
WNW 378781
 
4.9%
NW 369352
 
4.8%
Calm 368557
 
4.8%
SW 364470
 
4.7%
WSW 353806
 
4.6%
SSE 349110
 
4.5%
Other values (14) 3218746
41.6%

Length

2024-06-18T14:21:40.494164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 1330181
17.6%
s 419989
 
5.6%
ssw 384840
 
5.1%
w 383913
 
5.1%
wnw 378781
 
5.0%
nw 369352
 
4.9%
sw 364470
 
4.8%
wsw 353806
 
4.7%
sse 349110
 
4.6%
nnw 333427
 
4.4%
Other values (13) 2885319
38.2%

Most occurring characters

ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Wind_Speed(mph)
Real number (ℝ)

MISSING  ZEROS 

Distinct184
Distinct (%)< 0.1%
Missing571233
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean7.6854896
Minimum0
Maximum1087
Zeros961643
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:40.630499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7
Q310.4
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.4249834
Coefficient of variation (CV)0.7058735
Kurtosis1085.4752
Mean7.6854896
Median Absolute Deviation (MAD)3
Skewness8.0494761
Sum55006286
Variance29.430445
MonotonicityNot monotonic
2024-06-18T14:21:40.777624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 961643
 
12.4%
5 534875
 
6.9%
6 517199
 
6.7%
3 514123
 
6.7%
7 480904
 
6.2%
8 432522
 
5.6%
9 389161
 
5.0%
10 324080
 
4.2%
12 280269
 
3.6%
4.6 217615
 
2.8%
Other values (174) 2504770
32.4%
(Missing) 571233
 
7.4%
ValueCountFrequency (%)
0 961643
12.4%
1 195
 
< 0.1%
1.2 445
 
< 0.1%
2 451
 
< 0.1%
2.3 906
 
< 0.1%
3 514123
6.7%
3.5 203579
 
2.6%
4.6 217615
 
2.8%
5 534875
6.9%
5.8 216150
 
2.8%
ValueCountFrequency (%)
1087 1
 
< 0.1%
984 1
 
< 0.1%
822.8 7
< 0.1%
812 1
 
< 0.1%
703.1 2
 
< 0.1%
580 2
 
< 0.1%
518 2
 
< 0.1%
471.8 1
 
< 0.1%
328 1
 
< 0.1%
255 1
 
< 0.1%

Precipitation(in)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct299
Distinct (%)< 0.1%
Missing2203586
Missing (%)28.5%
Infinite0
Infinite (%)0.0%
Mean0.0084072098
Minimum0
Maximum36.47
Zeros4991718
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-06-18T14:21:40.912296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03
Maximum36.47
Range36.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11022465
Coefficient of variation (CV)13.110729
Kurtosis10710.92
Mean0.0084072098
Median Absolute Deviation (MAD)0
Skewness85.99914
Sum46448.22
Variance0.012149473
MonotonicityNot monotonic
2024-06-18T14:21:41.057268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4991718
64.6%
0.01 151010
 
2.0%
0.02 74008
 
1.0%
0.03 50055
 
0.6%
0.04 37300
 
0.5%
0.05 29167
 
0.4%
0.06 23958
 
0.3%
0.07 19353
 
0.3%
0.08 16138
 
0.2%
0.09 14307
 
0.2%
Other values (289) 117794
 
1.5%
(Missing) 2203586
28.5%
ValueCountFrequency (%)
0 4991718
64.6%
0.01 151010
 
2.0%
0.02 74008
 
1.0%
0.03 50055
 
0.6%
0.04 37300
 
0.5%
0.05 29167
 
0.4%
0.06 23958
 
0.3%
0.07 19353
 
0.3%
0.08 16138
 
0.2%
0.09 14307
 
0.2%
ValueCountFrequency (%)
36.47 1
 
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%
23.97 1
 
< 0.1%
10.8 1
 
< 0.1%
10.4 2
< 0.1%
10.18 1
 
< 0.1%
10.16 1
 
< 0.1%
10.14 2
< 0.1%
10.13 2
< 0.1%

Weather_Condition
Text

MISSING 

Distinct144
Distinct (%)< 0.1%
Missing173459
Missing (%)2.2%
Memory size59.0 MiB
2024-06-18T14:21:41.232331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length30
Mean length7.6729617
Min length3

Characters and Unicode

Total characters57968727
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowLight Rain
2nd rowLight Rain
3rd rowOvercast
4th rowMostly Cloudy
5th rowMostly Cloudy
ValueCountFrequency (%)
fair 2596473
24.8%
cloudy 2576033
24.6%
mostly 1032703
 
9.9%
clear 808743
 
7.7%
partly 709213
 
6.8%
light 545023
 
5.2%
rain 509071
 
4.9%
overcast 382866
 
3.7%
scattered 204829
 
2.0%
clouds 204829
 
2.0%
Other values (51) 888209
 
8.5%
2024-06-18T14:21:41.563110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Amenity
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7632060 
True
 
96334
ValueCountFrequency (%)
False 7632060
98.8%
True 96334
 
1.2%
2024-06-18T14:21:41.699717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Bump
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7724880 
True
 
3514
ValueCountFrequency (%)
False 7724880
> 99.9%
True 3514
 
< 0.1%
2024-06-18T14:21:41.791654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Crossing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
6854631 
True
873763 
ValueCountFrequency (%)
False 6854631
88.7%
True 873763
 
11.3%
2024-06-18T14:21:41.882771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Give_Way
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7691812 
True
 
36582
ValueCountFrequency (%)
False 7691812
99.5%
True 36582
 
0.5%
2024-06-18T14:21:41.976351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Junction
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7157052 
True
 
571342
ValueCountFrequency (%)
False 7157052
92.6%
True 571342
 
7.4%
2024-06-18T14:21:42.068232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

No_Exit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7708849 
True
 
19545
ValueCountFrequency (%)
False 7708849
99.7%
True 19545
 
0.3%
2024-06-18T14:21:42.159725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Railway
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7661415 
True
 
66979
ValueCountFrequency (%)
False 7661415
99.1%
True 66979
 
0.9%
2024-06-18T14:21:42.254719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Roundabout
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7728145 
True
 
249
ValueCountFrequency (%)
False 7728145
> 99.9%
True 249
 
< 0.1%
2024-06-18T14:21:42.345447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Station
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7526493 
True
 
201901
ValueCountFrequency (%)
False 7526493
97.4%
True 201901
 
2.6%
2024-06-18T14:21:42.438275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Stop
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7514023 
True
 
214371
ValueCountFrequency (%)
False 7514023
97.2%
True 214371
 
2.8%
2024-06-18T14:21:42.541129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Traffic_Calming
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7720796 
True
 
7598
ValueCountFrequency (%)
False 7720796
99.9%
True 7598
 
0.1%
2024-06-18T14:21:42.631633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
6584622 
True
1143772 
ValueCountFrequency (%)
False 6584622
85.2%
True 1143772
 
14.8%
2024-06-18T14:21:42.724888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Turning_Loop
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7728394 
ValueCountFrequency (%)
False 7728394
100.0%
2024-06-18T14:21:42.814705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Sunrise_Sunset
Categorical

Distinct2
Distinct (%)< 0.1%
Missing23246
Missing (%)0.3%
Memory size59.0 MiB
Day
5334553 
Night
2370595 

Length

Max length5
Median length3
Mean length3.6153276
Min length3

Characters and Unicode

Total characters27856634
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowDay

Common Values

ValueCountFrequency (%)
Day 5334553
69.0%
Night 2370595
30.7%
(Missing) 23246
 
0.3%

Length

2024-06-18T14:21:42.927753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:43.039661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
day 5334553
69.2%
night 2370595
30.8%

Most occurring characters

ValueCountFrequency (%)
D 5334553
19.2%
a 5334553
19.2%
y 5334553
19.2%
N 2370595
8.5%
i 2370595
8.5%
g 2370595
8.5%
h 2370595
8.5%
t 2370595
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27856634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5334553
19.2%
a 5334553
19.2%
y 5334553
19.2%
N 2370595
8.5%
i 2370595
8.5%
g 2370595
8.5%
h 2370595
8.5%
t 2370595
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27856634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5334553
19.2%
a 5334553
19.2%
y 5334553
19.2%
N 2370595
8.5%
i 2370595
8.5%
g 2370595
8.5%
h 2370595
8.5%
t 2370595
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27856634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5334553
19.2%
a 5334553
19.2%
y 5334553
19.2%
N 2370595
8.5%
i 2370595
8.5%
g 2370595
8.5%
h 2370595
8.5%
t 2370595
8.5%

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing23246
Missing (%)0.3%
Memory size59.0 MiB
Day
5695619 
Night
2009529 

Length

Max length5
Median length3
Mean length3.5216069
Min length3

Characters and Unicode

Total characters27134502
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 5695619
73.7%
Night 2009529
 
26.0%
(Missing) 23246
 
0.3%

Length

2024-06-18T14:21:43.169272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:43.281905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
day 5695619
73.9%
night 2009529
 
26.1%

Most occurring characters

ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%
Distinct2
Distinct (%)< 0.1%
Missing23246
Missing (%)0.3%
Memory size59.0 MiB
Day
6076156 
Night
1628992 

Length

Max length5
Median length3
Mean length3.4228321
Min length3

Characters and Unicode

Total characters26373428
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 6076156
78.6%
Night 1628992
 
21.1%
(Missing) 23246
 
0.3%

Length

2024-06-18T14:21:43.398847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:43.512647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
day 6076156
78.9%
night 1628992
 
21.1%

Most occurring characters

ValueCountFrequency (%)
D 6076156
23.0%
a 6076156
23.0%
y 6076156
23.0%
N 1628992
 
6.2%
i 1628992
 
6.2%
g 1628992
 
6.2%
h 1628992
 
6.2%
t 1628992
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26373428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 6076156
23.0%
a 6076156
23.0%
y 6076156
23.0%
N 1628992
 
6.2%
i 1628992
 
6.2%
g 1628992
 
6.2%
h 1628992
 
6.2%
t 1628992
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26373428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 6076156
23.0%
a 6076156
23.0%
y 6076156
23.0%
N 1628992
 
6.2%
i 1628992
 
6.2%
g 1628992
 
6.2%
h 1628992
 
6.2%
t 1628992
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26373428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 6076156
23.0%
a 6076156
23.0%
y 6076156
23.0%
N 1628992
 
6.2%
i 1628992
 
6.2%
g 1628992
 
6.2%
h 1628992
 
6.2%
t 1628992
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing23246
Missing (%)0.3%
Memory size59.0 MiB
Day
6377548 
Night
1327600 

Length

Max length5
Median length3
Mean length3.3446008
Min length3

Characters and Unicode

Total characters25770644
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowDay
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 6377548
82.5%
Night 1327600
 
17.2%
(Missing) 23246
 
0.3%

Length

2024-06-18T14:21:43.630465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-18T14:21:43.745508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
day 6377548
82.8%
night 1327600
 
17.2%

Most occurring characters

ValueCountFrequency (%)
D 6377548
24.7%
a 6377548
24.7%
y 6377548
24.7%
N 1327600
 
5.2%
i 1327600
 
5.2%
g 1327600
 
5.2%
h 1327600
 
5.2%
t 1327600
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25770644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 6377548
24.7%
a 6377548
24.7%
y 6377548
24.7%
N 1327600
 
5.2%
i 1327600
 
5.2%
g 1327600
 
5.2%
h 1327600
 
5.2%
t 1327600
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25770644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 6377548
24.7%
a 6377548
24.7%
y 6377548
24.7%
N 1327600
 
5.2%
i 1327600
 
5.2%
g 1327600
 
5.2%
h 1327600
 
5.2%
t 1327600
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25770644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 6377548
24.7%
a 6377548
24.7%
y 6377548
24.7%
N 1327600
 
5.2%
i 1327600
 
5.2%
g 1327600
 
5.2%
h 1327600
 
5.2%
t 1327600
 
5.2%

Interactions

2024-06-18T14:17:52.101908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:04.560117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:15.295268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:25.654185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:33.857723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:41.879905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:51.795421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:02.329629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:11.591999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:22.270147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:32.273938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:42.472874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:52.845692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:05.486202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:16.186681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:26.320643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:34.529810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:42.718741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:52.837518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:03.118054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:12.560438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:23.177167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:33.263035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:43.336665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:53.411153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:06.175972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:16.848236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:26.958242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:35.202036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:43.332355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:53.500932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:03.717109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:13.211422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:23.802440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:33.902565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:43.954447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:53.965544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:06.833504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:17.513804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:27.616968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:35.831305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:43.959621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:54.165909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:04.305537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:13.863683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:24.415623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:34.528566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:44.536023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:54.695329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:07.739042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:18.495533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:28.281357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:36.476399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:44.775213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:55.189476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:05.092330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:14.834624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:25.330593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:35.487513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:45.425417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:55.412617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:08.697031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:19.511127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:28.955742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:37.130854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:45.664960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:56.145413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:05.877087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:15.798537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:26.233752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:36.409736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:46.310641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:56.123522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:09.504104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:20.310388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:29.734177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:37.728388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:46.438092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:56.937071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:06.662483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:16.600162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:26.993680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:37.178643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:47.076256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:56.841492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:10.469896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:21.336167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:30.554005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:38.364113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:47.342631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:57.886974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:07.454127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:17.560933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:27.888823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:38.098940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:47.963012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:57.567370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:11.479702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:22.294315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:31.239201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:39.005413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:48.258704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:58.846024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:08.251433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:18.534224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:28.802286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:39.033023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:48.822973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:58.293937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:12.641546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:23.271432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:31.893162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:39.667654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:49.166076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:59.813797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:09.049420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:19.520180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:29.725654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:39.958579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:49.688510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:59.016804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:13.567676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:24.199149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:32.594317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:40.304129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:50.065643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:00.751079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:09.851599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:20.482751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:30.607349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:40.846771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:50.563197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:59.734092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:14.350939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:24.980583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:33.191308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:40.872449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:16:50.812601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:01.523056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:10.601237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:21.336976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:31.357418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:41.597312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-18T14:17:51.301540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-18T14:18:07.858015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-18T14:18:42.176492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-18T14:20:31.612246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSourceSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionStreetCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilight
0A-1Source232016-02-08 05:46:002016-02-08 11:00:0039.865147-84.058723NaNNaN0.01Right lane blocked due to accident on I-70 Eastbound at Exit 41 OH-235 State Route 4.I-70 EDaytonMontgomeryOH45424USUS/EasternKFFO2016-02-08 05:58:0036.9NaN91.029.6810.0CalmNaN0.02Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightNight
1A-2Source222016-02-08 06:07:592016-02-08 06:37:5939.928059-82.831184NaNNaN0.01Accident on Brice Rd at Tussing Rd. Expect delays.Brice RdReynoldsburgFranklinOH43068-3402USUS/EasternKCMH2016-02-08 05:51:0037.9NaN100.029.6510.0CalmNaN0.00Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightDay
2A-3Source222016-02-08 06:49:272016-02-08 07:19:2739.063148-84.032608NaNNaN0.01Accident on OH-32 State Route 32 Westbound at Dela Palma Rd. Expect delays.State Route 32WilliamsburgClermontOH45176USUS/EasternKI692016-02-08 06:56:0036.033.3100.029.6710.0SW3.5NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseNightNightDayDay
3A-4Source232016-02-08 07:23:342016-02-08 07:53:3439.747753-84.205582NaNNaN0.01Accident on I-75 Southbound at Exits 52 52B US-35. Expect delays.I-75 SDaytonMontgomeryOH45417USUS/EasternKDAY2016-02-08 07:38:0035.131.096.029.649.0SW4.6NaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightDayDayDay
4A-5Source222016-02-08 07:39:072016-02-08 08:09:0739.627781-84.188354NaNNaN0.01Accident on McEwen Rd at OH-725 Miamisburg Centerville Rd. Expect delays.Miamisburg Centerville RdDaytonMontgomeryOH45459USUS/EasternKMGY2016-02-08 07:53:0036.033.389.029.656.0SW3.5NaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseDayDayDayDay
5A-6Source232016-02-08 07:44:262016-02-08 08:14:2640.100590-82.925194NaNNaN0.01Accident on I-270 Outerbelt Northbound near Exit 29 OH-3 State St. Expect delays.Westerville RdWestervilleFranklinOH43081USUS/EasternKCMH2016-02-08 07:51:0037.935.597.029.637.0SSW3.50.03Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
6A-7Source222016-02-08 07:59:352016-02-08 08:29:3539.758274-84.230507NaNNaN0.00Accident on Oakridge Dr at Woodward Ave. Expect delays.N Woodward AveDaytonMontgomeryOH45417-2476USUS/EasternKDAY2016-02-08 07:56:0034.031.0100.029.667.0WSW3.5NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7A-8Source232016-02-08 07:59:582016-02-08 08:29:5839.770382-84.194901NaNNaN0.01Accident on I-75 Southbound at Exit 54B Grand Ave. Expect delays.N Main StDaytonMontgomeryOH45405USUS/EasternKDAY2016-02-08 07:56:0034.031.0100.029.667.0WSW3.5NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
8A-9Source222016-02-08 08:00:402016-02-08 08:30:4039.778061-84.172005NaNNaN0.00Accident on Notre Dame Ave at Warner Ave. Expect delays.Notre Dame AveDaytonMontgomeryOH45404-1923USUS/EasternKFFO2016-02-08 07:58:0033.3NaN99.029.675.0SW1.2NaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
9A-10Source232016-02-08 08:10:042016-02-08 08:40:0440.100590-82.925194NaNNaN0.01Right hand shoulder blocked due to accident on I-270 Outerbelt Westbound at Exit 29 OH-3 State St.Westerville RdWestervilleFranklinOH43081USUS/EasternKCMH2016-02-08 08:28:0037.433.8100.029.623.0SSW4.60.02Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
IDSourceSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionStreetCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilight
7728384A-7777752Source122019-08-23 17:42:272019-08-23 18:11:1034.064460-118.00388034.065330-117.9971500.390At I-605 - Accident.I-10 EBaldwin ParkLos AngelesCA91706USUS/PacificKEMT2019-08-23 17:53:0078.078.052.029.6910.0VAR6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728385A-7777753Source122019-08-23 17:40:122019-08-23 18:08:3533.943599-117.07788033.943599-117.0778800.000At Jack Rabbit Trl - Accident.CA-60 EMoreno ValleyRiversideCA92555USUS/PacificKRIV2019-08-23 17:58:0088.088.032.028.2010.0WNW10.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728386A-7777754Source122019-08-23 17:40:122019-08-23 18:08:3534.261030-119.22800034.262390-119.2308700.189At Telephone Rd/Exit 65 - Accident.El Camino Real NVenturaVenturaCA93003USUS/PacificKOXR2019-08-23 17:51:0073.073.068.029.7610.0W9.00.0FairFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728387A-7777755Source122019-08-23 17:43:562019-08-23 18:12:2733.741700-117.83709033.739170-117.8300100.443At CA-55 - Accident.Santa Ana Fwy STustinOrangeCA92780USUS/PacificKSNA2019-08-23 17:53:0075.075.060.029.7410.0SSW9.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728388A-7777756Source122019-08-23 18:30:232019-08-23 18:58:5434.239104-118.41617634.239104-118.4161760.000At Osborne St/Exit 154 - Accident.Golden State Fwy NPacoimaLos AngelesCA91331USUS/PacificKWHP2019-08-23 18:50:0081.081.048.028.7810.0ESE6.0NaNFairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728389A-7777757Source122019-08-23 18:03:252019-08-23 18:32:0134.002480-117.37936033.998880-117.3709400.543At Market St - Accident.Pomona Fwy ERiversideRiversideCA92501USUS/PacificKRAL2019-08-23 17:53:0086.086.040.028.9210.0W13.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728390A-7777758Source122019-08-23 19:11:302019-08-23 19:38:2332.766960-117.14806032.765550-117.1536300.338At Camino Del Rio/Mission Center Rd - Accident.I-8 WSan DiegoSan DiegoCA92108USUS/PacificKMYF2019-08-23 18:53:0070.070.073.029.3910.0SW6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728391A-7777759Source122019-08-23 19:00:212019-08-23 19:28:4933.775450-117.84779033.777400-117.8572700.561At Glassell St/Grand Ave - Accident. in the right lane.Garden Grove FwyOrangeOrangeCA92866USUS/PacificKSNA2019-08-23 18:53:0073.073.064.029.7410.0SSW10.00.0Partly CloudyFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728392A-7777760Source122019-08-23 19:00:212019-08-23 19:29:4233.992460-118.40302033.983110-118.3956500.772At CA-90/Marina Fwy/Jefferson Blvd - Accident.San Diego Fwy SCulver CityLos AngelesCA90230USUS/PacificKSMO2019-08-23 18:51:0071.071.081.029.6210.0SW8.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay
7728393A-7777761Source122019-08-23 18:52:062019-08-23 19:21:3134.133930-117.23092034.137360-117.2393400.537At Highland Ave/Arden Ave - Accident.CA-210 WHighlandSan BernardinoCA92346USUS/PacificKSBD2019-08-23 20:50:0079.079.047.028.637.0SW7.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDay